Managing interest rate and liquidity risk on savings and current accounts is a hot topic for banks in 2021. Risk, ALM, and treasury managers have to navigate changing regulatory requirements, changing withdrawal behavior and deposit pricing strategies due to COVID-19, and decreasing market rates.
With the trend towards increasing computational resources and larger datasets, the application of machine learning (ML) in finance has gained attraction. Financial Institutions are interested in how and where ML models can be of added value in their business model.
Currently, for many organizations, operational resilience is at the top of the agenda of the Board and senior management. The COVID-19 pandemic clearly showed how vulnerable societies and organizations can be to unexpected and unforeseen events.
Climate and environmental changes are viewed among the most important risks in society at present. As the financial sector is key for the transition towards a low-carbon and more circular economy, financial institutions have to deal with climate-related and environmental financial risks (C&E risks). At the same time, the increased importance of these C&E risks also presents new business opportunities for the financial sector. Therefore, to support banks in their self-assessment and action plans, Zanders developed a Scan & Plan Solution on C&E risks.
According to Moore’s law, computing power doubles up each two years. This performance increase in computing power makes machine learning increasingly efficient each year, and widely applicable. But does this also apply to credit risk issues?